{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification by Wine Type\n",
    "\n",
    "## Wine Data\n",
    "Data from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "\n",
    "### Citations\n",
    "P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. \n",
    "Modeling wine preferences by data mining from physicochemical properties.\n",
    "In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.\n",
    "\n",
    "Available at:\n",
    "- [@Elsevier](http://dx.doi.org/10.1016/j.dss.2009.05.016)\n",
    "- [Pre-press (pdf)](http://www3.dsi.uminho.pt/pcortez/winequality09.pdf)\n",
    "- [bib](http://www3.dsi.uminho.pt/pcortez/dss09.bib)\n",
    "\n",
    "Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_wine = pd.read_csv('data/winequality-red.csv')\n",
    "white_wine = pd.read_csv('data/winequality-white.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at quality scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_quality_scores(df, kind):\n",
    "    ax = df.quality.value_counts().sort_index(\n",
    "        ascending=False\n",
    "    ).plot.barh(title=f'{kind.title()} Wine Quality Scores', figsize=(12, 3))\n",
    "    for bar in ax.patches:\n",
    "        ax.text(\n",
    "            bar.get_width(), \n",
    "            bar.get_y() + bar.get_height()/4, \n",
    "            f'{bar.get_width()/df.shape[0]:.1%}'\n",
    "        )\n",
    "    plt.xlabel('count of wines')\n",
    "    plt.ylabel('quality score')\n",
    "\n",
    "plot_quality_scores(white_wine, 'white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_quality_scores(red_wine, 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining red and white wine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.081</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.99689</td>\n",
       "      <td>3.59</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.13</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.044</td>\n",
       "      <td>26.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.99772</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.049</td>\n",
       "      <td>17.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.99370</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.63</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.075</td>\n",
       "      <td>10.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.00005</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.84</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.122</td>\n",
       "      <td>45.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.99360</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.31</td>\n",
       "      <td>8.7</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "848             6.4              0.64         0.21             1.8      0.081   \n",
       "2529            6.6              0.42         0.13            12.8      0.044   \n",
       "131             5.6              0.50         0.09             2.3      0.049   \n",
       "244            15.0              0.21         0.44             2.2      0.075   \n",
       "1551            6.6              0.19         0.99             1.2      0.122   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "848                  14.0                  31.0  0.99689  3.59       0.66   \n",
       "2529                 26.0                 158.0  0.99772  3.24       0.47   \n",
       "131                  17.0                  99.0  0.99370  3.63       0.63   \n",
       "244                  10.0                  24.0  1.00005  3.07       0.84   \n",
       "1551                 45.0                 129.0  0.99360  3.09       0.31   \n",
       "\n",
       "      alcohol  quality   kind  \n",
       "848       9.8        5    red  \n",
       "2529      9.0        5  white  \n",
       "131      13.0        5    red  \n",
       "244       9.2        7    red  \n",
       "1551      8.7        6  white  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = pd.concat([\n",
    "    white_wine.assign(kind='white'), red_wine.assign(kind='red')\n",
    "])\n",
    "wine.sample(5, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No null data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6497 entries, 0 to 1598\n",
      "Data columns (total 13 columns):\n",
      "fixed acidity           6497 non-null float64\n",
      "volatile acidity        6497 non-null float64\n",
      "citric acid             6497 non-null float64\n",
      "residual sugar          6497 non-null float64\n",
      "chlorides               6497 non-null float64\n",
      "free sulfur dioxide     6497 non-null float64\n",
      "total sulfur dioxide    6497 non-null float64\n",
      "density                 6497 non-null float64\n",
      "pH                      6497 non-null float64\n",
      "sulphates               6497 non-null float64\n",
      "alcohol                 6497 non-null float64\n",
      "quality                 6497 non-null int64\n",
      "kind                    6497 non-null object\n",
      "dtypes: float64(11), int64(1), object(1)\n",
      "memory usage: 685.2+ KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have more whites than reds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white    4898\n",
       "red      1599\n",
       "Name: kind, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.kind.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to understand if chemical properties can be used to determine wine type. Unfortunately, `describe()` gives a very long output, so we need a visualization to compare the wines this way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">alcohol</th>\n",
       "      <th colspan=\"2\" halign=\"left\">chlorides</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total sulfur dioxide</th>\n",
       "      <th colspan=\"8\" halign=\"left\">volatile acidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>1599.0</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>1599.0</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>4898.0</td>\n",
       "      <td>10.514267</td>\n",
       "      <td>1.230621</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>11.4</td>\n",
       "      <td>14.2</td>\n",
       "      <td>4898.0</td>\n",
       "      <td>0.045772</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>4898.0</td>\n",
       "      <td>0.278241</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      alcohol                                                  chlorides  \\\n",
       "        count       mean       std  min  25%   50%   75%   max     count   \n",
       "kind                                                                       \n",
       "red    1599.0  10.422983  1.065668  8.4  9.5  10.2  11.1  14.9    1599.0   \n",
       "white  4898.0  10.514267  1.230621  8.0  9.5  10.4  11.4  14.2    4898.0   \n",
       "\n",
       "                 ...  total sulfur dioxide        volatile acidity            \\\n",
       "           mean  ...                   75%    max            count      mean   \n",
       "kind             ...                                                           \n",
       "red    0.087467  ...                  62.0  289.0           1599.0  0.527821   \n",
       "white  0.045772  ...                 167.0  440.0           4898.0  0.278241   \n",
       "\n",
       "                                               \n",
       "            std   min   25%   50%   75%   max  \n",
       "kind                                           \n",
       "red    0.179060  0.12  0.39  0.52  0.64  1.58  \n",
       "white  0.100795  0.08  0.21  0.26  0.32  1.10  \n",
       "\n",
       "[2 rows x 88 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.drop(columns='quality').groupby('kind').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How do chemical properties of the wine correlate to each other and the wine type?\n",
    "It's important to perform an in-depth exploration of the data before modeling. This includes consulting domain experts, looking for correlations between variables, examining distributions, etc. The visualizations covered in chapters [5](https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/tree/master/ch_05) and [6](https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/tree/master/ch_06) will prove indispensible for this process. One such visualization is the heatmap. In order to predict if the wine is red or white, we would look for correlations between chemical properties and wine type. We would also try to see if there is a difference in the distribution of our variables for white versus red wines. Some other helpful plot types include box plots, pair plots, and the scatter matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dd09d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 7))\n",
    "sns.heatmap(\n",
    "    wine.drop(columns='quality').assign(\n",
    "        is_red=lambda x: np.where(x.kind == 'red', 1, 0)\n",
    "    ).corr(), center=0, square=True, annot=True, fmt='.1g'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of Red and White Wines by Their Chemical Properties\n",
    "This visualization will be easier to digest than the output of `describe()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Comparing Chemical Properties of Red and White Wines')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "chemical_properties = [col for col in wine.columns if col not in ['quality', 'kind']]\n",
    "melted = wine.drop(columns='quality').melt(id_vars=['kind'])\n",
    "\n",
    "fig, axes = plt.subplots(math.ceil(len(chemical_properties) / 4), 4, figsize=(20, 10))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for prop, ax in zip(chemical_properties, axes):\n",
    "    sns.boxplot(\n",
    "        data=melted[melted.variable.isin([prop])], \n",
    "        x='variable', y='value', hue='kind', ax=ax\n",
    "    )\n",
    "    \n",
    "# remove the extra subplots\n",
    "for ax in axes[len(chemical_properties):]:\n",
    "    ax.remove()\n",
    "    \n",
    "plt.suptitle('Comparing Chemical Properties of Red and White Wines')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification of Red and White Wines\n",
    "1. separate x and y\n",
    "2. get the training and testing set\n",
    "3. build a pipeline with standard scaler followed by logistic regression and fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 1\n",
    "wine_y = np.where(wine.kind == 'red', 1, 0)\n",
    "wine_X = wine.drop(columns=['quality', 'kind'])\n",
    "\n",
    "# 2\n",
    "w_X_train, w_X_test, w_y_train, w_y_test = train_test_split(\n",
    "    wine_X, wine_y, test_size=0.25, random_state=0, stratify=wine_y\n",
    ")\n",
    "\n",
    "# 3\n",
    "white_or_red = Pipeline([\n",
    "    ('scale', StandardScaler()), ('lr', LogisticRegression(solver='lbfgs', random_state=0))\n",
    "]).fit(w_X_train, w_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "kind_preds = white_or_red.predict(w_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. evaluate predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a confusion matrix to see how the model's predictions align with the actual class labels. The model only made 13 incorrect predictions; we will look into these in [chapter 10](https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas/blob/master/ch_10/wine.ipynb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14548ef0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual\n",
    "\n",
    "confusion_matrix_visual(w_y_test, kind_preds, ['white', 'red'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Precision, recall, and F1 score all look good with this model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1225\n",
      "           1       0.99      0.98      0.98       400\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1625\n",
      "   macro avg       0.99      0.99      0.99      1625\n",
      "weighted avg       0.99      0.99      0.99      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(w_y_test, kind_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC Curves\n",
    "Visualize model performance using true positive rates and false positive rates. The area under the curve is on [0, 1] with 1 being the best. This visualization allows us to compare our model to the baseline of random guessing (AUC of 0.5), as well as, other models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import ticker\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "data = pd.read_csv('data/sample_roc_curves.csv')\n",
    "\n",
    "ax = sns.lineplot(\n",
    "    data=data, hue='label', x='x', y='y', palette='Greens'\n",
    ")\n",
    "\n",
    "ax.plot([0, 1], [0, 1], 'k--', alpha=0.3)\n",
    "\n",
    "# formatting \n",
    "ax.set_title('Sample ROC Curves')\n",
    "ax.set_xlabel('False Positive Rate (FPR)')\n",
    "ax.set_ylabel('True Positive Rate (TPR)')\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "for i, label in enumerate(labels[1:]):\n",
    "    curve_data = data.query(f'label == \"{label}\"')\n",
    "    labels[1 + i] = f'{label}; AUC is {auc(curve_data.x, curve_data.y):.2}'\n",
    "ax.legend(handles=handles[1:], labels=labels[1:])\n",
    "\n",
    "ax.xaxis.set_major_formatter(ticker.PercentFormatter(xmax=1))\n",
    "ax.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC curves are also called sensitivity-specificity plots since they plot sensitivity (TPR) versus 1-specificity (FPR). They include all sections of the confusion matrix, which is why in cases of class balance, ROC curves are optimistic of performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Portion of Confusion Matrix Considered')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "\n",
    "ax = sns.heatmap(\n",
    "    np.array([[1, 0], [1, 0]]), cbar=False, cmap=ListedColormap(['whitesmoke', 'whitesmoke']),\n",
    "    annot=np.array([\n",
    "        ['sensitivity', 'specificity'], \n",
    "        ['sensitivity', 'specificity']\n",
    "    ]), fmt='', annot_kws={'size': 12, 'weight': 'bold'}, linewidths=0.3, linecolor='black'\n",
    ")\n",
    "ax.set_xticklabels([True, False])\n",
    "ax.set_xlabel('Actual', fontsize=12)\n",
    "ax.set_yticklabels([True, False], rotation=0)\n",
    "ax.set_ylabel('Predicted', fontsize=12)\n",
    "ax.set_title('Portion of Confusion Matrix Considered', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model performs very well, the area under the curve (AUC) is nearly 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1464ae70>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_roc\n",
    "\n",
    "plot_roc(w_y_test, white_or_red.predict_proba(w_X_test)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision-recall curves\n",
    "When faced with class imbalance, we use precision-recall curves since ROC curves will be optimistic of model performance. AP is the weighted average precision and AUC is the area under the curve once again on [0, 1]. The baseline is now the percentage of observations belonging to the positive class. Values below this line are worse than random:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f62a90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ml_utils.classification import plot_pr_curve\n",
    "\n",
    "plot_pr_curve(w_y_test, white_or_red.predict_proba(w_X_test)[:,1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
